In this paper, the CuCr2O4 spinel catalyst was synthesized by the Pechini method, and its activity was evaluated in catalytic oxidation of CO. CuCr2O4 spinel catalyst was characterized by XRD, BET, H2-TPR, and SEM. This catalyst has a good ability in CO oxidation. The effects of three synthesis variables (EG/citrate, citrate/nitrate ratio, and calcination temperature) and reaction temperature as an operational variable on CO conversion were investigated. Based on the results, the optimum neural network architecture succeeded to predict CO conversion data with an acceptable level of accuracy. The model predicted that the relative importance of variables is as follows: calcination temperature > citrate/nitrate ratio > EG/citrate ratio. The optimum neural network architecture was used as a fitness function for the genetic algorithm to find the optimum catalyst. Under the optimum condition (EG/citrate: 3.24, citrate/nitrate ratio: 0.62 and calcination temperature: 620 °C), the predicted optimum value of CO conversion was found to be in a good agreement with the corresponding actual value.